---
title: "CO2 Dashboard by Fung Yi"
#runtime: shiny
output:
  flexdashboard::flex_dashboard:
    theme: 
      version: 4
      bootswatch: minty
    orientation: rows
    source_code: embed
    navbar:
      - { title: "", href: "https://sites.google.com/view/fungyi/", align: right, icon: fa-house-user}
      - { title: "", href: "https://www.linkedin.com/in/fungyileung/", align: right, icon: fa-linkedin }
---


```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(ggvis)
```

```{r}
# source of data : https://nyc3.digitaloceanspaces.com/owid-public/data/co2/owid-co2-data.csv
data <- read.csv("owid-co2-data.csv")
#focus_year <-max(data$year)
focus_year <-2018
continent <- c("Asia", "Africa","Europe","North America","South America","Oceania","Antarctica")
```

```{r}
mycolors <- c("#354e5c","#75b8d1","#c9d175","#758bd1","#d1ab75","#d175b8")
```

```{r, include= TRUE}
htmltools::tagList(fontawesome::fa_html_dependency())
```

Interactive Data Visualization
=====================================








Row
-------------------------------------
### World Annual 
CO2 emissions 

```{r}
valueBox(paste("Yr ",focus_year),
         color = "danger",
         icon="fa-globe")
```

### Annual production (million tonnes)

```{r}
x <- data %>% 
        filter(country == "World")  %>% 
        filter(year == focus_year) %>% 
        select(co2)
        
valueBox(round(x),
         icon = "fa-users")
```

### From Coal 
(million tonnes)

```{r}
x <- data %>% 
        filter(country == "World")  %>% 
        filter(year == focus_year) %>% 
        select(coal_co2)
valueBox(round(x),
         icon = 'fa-cloud')
```


### From Oil 
 (million tonnes)

```{r}
x <- data %>% 
        filter(country == "World")  %>% 
        filter(year == focus_year) %>% 
        select(oil_co2)
valueBox(round(x),
         icon = 'fa fa-tint')
```

### From Gas 
 (million tonnes)

```{r}
x <- data %>% 
        filter(country == "World")  %>% 
        filter(year == focus_year) %>% 
        select(gas_co2)
valueBox(round(x),
         icon = 'fa-skyatlas')
```


### From Cement (million tonnes)

```{r}
x <- data %>% 
        filter(country == "World")  %>% 
        filter(year == focus_year) %>% 
        select(cement_co2)
valueBox(round(x),
         icon = 'fa-building')
```



### From Flaring (million tonnes)

```{r}
x <- data %>% 
        filter(country == "World")  %>% 
        filter(year == focus_year) %>% 
        select(flaring_co2)
valueBox(round(x),
         icon = 'fa-fire')
```

Row
-------------------------------
### Annual CO2 Emission By Top 5 countries

```{r}

p1 <- data %>%
         filter(iso_code!="") %>%
         filter(year == focus_year)  %>%
         filter(country !="World")  %>%
         arrange(desc(co2)) %>%
         slice(1:5) %>%
         plot_ly(x = ~country,
                 y = ~co2,
                 color = "secondary",
                 type = 'bar') %>%
layout(
yaxis = list( title = '(million tonnes)')
)
p1
```

### Annual CO2 Distribution by continent

```{r}
p2 <- data %>%
         filter(year == focus_year)  %>%
         filter(country %in% continent)  %>%
         plot_ly(labels = ~country,
                 values = ~co2,
                 marker = list(colors = "info")) %>%
         add_pie(hole = 0.2) %>%
         layout(xaxis = list(zeroline = F,
                             showline = F,
                             showticklabels = F,
                             showgrid = F),
                yaxis = list(zeroline = F,
                             showline = F,
                             showticklabels=F,
                             showgrid=F))
p2


```

### GDP per capita Vs CO2 percaptia emission

```{r}
p3 <- data %>%
         filter(iso_code!="") %>%
         filter(year == focus_year)  %>%
         filter(country !="World")  %>%
         mutate(gdp_per_capita = gdp/population) %>%
          plot_ly(x = ~gdp_per_capita,
                 y = ~co2_per_capita,
                 
                 type = 'scatter') %>%
layout(
yaxis = list( title = '(million tonnes)')
)
p3
```

Map
========================================

### Annual CO2 Emission per Country
```{r}
co2_per_country <- data %>%
         filter(iso_code!="") %>%
         filter(year == focus_year)  %>%
         filter(country !="World") %>%
         group_by(country)


hcmap(
  map = "custom/world",
  data = co2_per_country,
  name = "million tonnes",
  joinBy =  c("iso-a3", "iso_code"), value = "co2"
  #joinBy =  c("name", "country"), value = "co2"
)
```

Data Table
========================================

```{r}
data_focus_year<- data %>%
         filter(year == focus_year)

datatable(data_focus_year,
          caption = paste("Data in Year ",focus_year) ,
          rownames = T,
          filter = "top",
          options = list(pageLength = 25))
```

Pivot Table
=========================================

```{r}
data_continent <- data %>%
        # filter(country %in% continent) %>%
         filter(year %in% (1980:2020)) %>%
          select(country, year, co2_per_capita, cement_co2_per_capita, coal_co2_per_capita,oil_co2_per_capita)


rpivotTable(data_continent,
            aggregatorName = "Average",
            cols= "country",
            rows ="year" ,
            vals = "co2_per_capita",
            rendererName = "Heatmap"
            )
```